Multi-component peptide hydrogels – a systematic study incorporating biomolecules for the exploration of diverse, tuneable biomaterials
Bibliographic record
Abstract
Peptide-based supramolecular gels can be designed to be functional "smart" materials that have applications in drug delivery, tissue engineering, and supramolecular chemistry. Although many multi-component gel systems have been designed and reported, many of these applications still rely solely on single-component gel systems which limits the functionalities of the materials. Multi-component self-assembly leads to the formation of highly ordered and complex architectures while offering the possibility to generate hydrogels with interesting properties including functional complexity and diverse morphologies. Being able to incorporate various classes of biomolecules can allow for tailoring the materials' functionalities to specific application needs. Here, a novel peptide amphiphile, myristyl-Phe-Phe (C14-FF), was synthesized and explored for hydrogel formation. The hydrogel possesses a nanofiber matrix morphology, composed of β-sheet aggregates, a record-low gelation concentration for this class of compounds, and a unique solvent-dependent helical switch. The C14-FF hydrogel was then explored with various classes of biomolecules (carbohydrates, vitamins, proteins, building blocks of HA) to generate a multi-component library of gels that have potential to represent the complex natural extracellular matrix. Selected multi-component gels exhibit an excellent compatibility with mesenchymal stem cells showing high cell viability percentages, which holds great promise for applications in regenerative therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".